This paper examines confidence intervals for class prevalences in shifted datasets.
problem Estimating class prevalences in shifted datasets and distinguishing between confidence and prediction intervals.
method Simulation study comparing different methods for constructing confidence and prediction intervals.
result Discriminatory power of the classifier affects the accuracy of class prevalence estimates.
Bayesian method corrects bias in imbalanced datasets.
problem Prevalence bias in machine learning datasets.
method Bayesian risk minimization framework, bias-corrected loss function.
result Corrected loss function improves model performance.
The paper discusses thresholds and bounds for accuracy in binary classification systems.
problem The accuracy of binary classification systems and its dependence on prevalence.
method Analyzing the precision-prevalence curve and negative predictive value-prevalence curve to find thresholds and bounds.
result Thresholds (φe and φn) bound various accuracy metrics (Fβ, F1, FM, MCC) and the ratio of maximum accuracy to prevalence. LEMPS predicts malaria prevalence with high accuracy in Ibadan, West Africa.
problem Inadequate malaria prediction systems in highly endemic countries.
method Developed and validated a Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) using 22-years of prospective data.
result LEMPS achieves good generalization performance, predicting monthly prevalence with MAE<=6x10-2 and MSE<=7x10-3.
Proposes a method to improve rare event prediction in healthcare.
problem Rare event classification in healthcare with low prevalence labels.
method Variational disentanglement approach to semi-parametric learning.
result Outperforms existing alternatives in mortality prediction on COVID-19 cohort.
New method adapts to structural shifts in graph data for better label prevalence estimation.
problem Structural shifts in graph data affect label prevalence estimation.
method Importance sampling variant of KDEy quantification approach.
result Adapts to structural shifts and outperforms standard approaches.
New conformal prediction methods for long-tailed classification problems.
problem Rare classes are systematically omitted in existing conformal prediction methods.
method Introduced a new conformal score function and a new interpolation procedure.
result Smoothly trade off set size and class-conditional coverage.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
problem Early detection of CKD in diabetic patients.
method Sparse longitudinal representations of EHR data.
result Proposed model achieves higher predictive performance than baselines.
Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.
problem Uncertainty quantification in prevalence estimation.
method Bayesian quantification methods, focusing on precision and coverage.
result PQ provides more precise and well-calibrated uncertainty quantification.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
problem Accurate measurement of content violations that are often rare and costly to label.
method Design-based measurement system using ML-assisted probability sampling and LLM labeling.
result Produces unbiased prevalence estimates with confidence intervals and dashboard drilldowns.
Classification is the task of predicting the class labels of objects based on the observation of their features. In contrast, quantification has been defined as the task of determining the prevalences of the different sorts of class labels in a target dataset. The simplest approach to quantification is Classify & Count…
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns …
Paper explores how unsupervised learning can be understood through linear algebra concepts.
problem Understanding unsupervised learning through linear algebra concepts.
method Introducing the concept of linearly independent populations and using them to solve for prevalence values.
result Unsupervised learning can be realized as a generalization of supervised learning.
Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy c…
Study uses machine learning to predict rain in Australia.
problem Challenging task of predicting rainfall with uncertain outcomes.
method Machine learning techniques, including modeling inputs, methods, and pre-processing.
result Comparison of various machine learning techniques' reliability in predicting rainfall.
This study connects prevalence and machine learning for diagnostic testing.
problem Uncertainty quantification in machine learning for diagnostic tests.
method Developed a numerical homotopy algorithm to estimate classification boundaries and quantify uncertainty.
result The proposed method stabilizes uncertainty quantification in machine learning for diagnostic tests.
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
problem Quantification of class prevalence in bags of examples.
method Permutation-invariant Histograms and deep neural networks.
result HistNetQ outperforms other quantification methods and optimizes custom loss functions.
The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%…
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model w…
Improved video tracking accuracy with active learning.
problem Efficiently predicting object matches in videos with active learning.
method Adversarial approach for active learning with structured prediction domains.
result Better accuracy and computational efficiency for object tracking.
Deep learning predicts asthma ED visits better than traditional methods.
problem Predicting asthma-related ED visits to improve patient management.
method Deep learning (Artificial Neural Networks) compared to Lasso logistic regression.
result Deep learning model (ANN) outperforms traditional Lasso logistic regression (AUC = 0.845 vs. AUC = 0.842).
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
Bayesian methods improve group testing for identifying infected patients.
problem Identifying infected patients from group testing results with false positives.
method Bayesian inference and belief propagation algorithm, combined with expectation-maximization method.
result True-positive rate improved by considering credible intervals.
New method learns distribution shifts caused by predictive models in social computing.
problem Learning distribution shifts due to predictive models in social computing.
method Reverse causal model with microfoundation for agents' actions.
result Effective in minimizing performative prediction risk.
proxymate diagnoses and corrects proxy estimates for reliable inference.
problem Systematic bias in proxy estimates can lead to invalid inference on primary outcomes.
method Four-level framework for proxy validation and adjustment, including diagnostic checks and targeted corrections.
result Facilitated quick decision making on thousands of experiments at Meta.
Paper proposes a probabilistic method to handle missing data in decision trees.
problem Handling missing data in decision trees.
method At deployment time, use density estimators to compute expected predictions. At learning time, fine-tune tree parameters to minimize expected prediction loss.
result Effective compared to baselines in experiments.
Unified Bayesian framework for missing data imputation and prediction in clinical time series.
problem High prevalence of missing values in clinical time series data.
method Unified Bayesian recurrent framework for imputation and prediction.
result Strong performance gains over state-of-the-art methods on mortality prediction tasks.
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique t…
Imputation for prediction often offers limited benefits, especially with powerful models.
problem The challenge of missing data in predictive models.
method Comparative analysis of imputation methods across various predictive models and datasets.
result Advanced imputation methods often offer limited benefits for powerful predictive models.
Extends diffusion models to handle exponential family distributions for inverse problems.
problem Intractability of likelihood score for non-Gaussian observations.
method Evidence trick to approximate likelihood score for exponential family distributions.
result Effective Bayesian inference on complex Poisson processes and malaria prevalence prediction.
New method predicts sets under unknown covariate shift with high confidence.
problem Adapting to unknown covariate shift in prediction sets.
method PredSet-1Step, a flexible distribution-free method.
result Achieves asymptotic probably approximately correct coverage.
The paper proves prevalent existence and partially determines moduli space of area-minimizing surfaces with fractal singular sets.
problem Existence and moduli space of area-minimizing surfaces with fractal singular sets.
method Proof of prevalent existence, determination of moduli space, refinement of strata.
result Sharp results on moduli space and refinement of strata, showing fractal singularities do not completely dissolve under generic perturbations.
New method speeds up deep learning optimization.
problem Scalable second-order optimization for deep learning.
method Second-order optimization with algorithmic and numerical improvements.
result Significant convergence and wall-clock time improvements.
Personalized models using group attributes reduce performance, study finds.
problem Reducing performance of models using group attributes like race or gender.
method Formal conditions and collective preference guarantees to ensure fair use.
result Models personalized with group attributes reduce performance at a group level.
In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document level topic proportions with covariates and dynamic structure…
Novel Bayesian method for high-dimensional count data prediction.
problem Count data in high-dimensional settings requires feature selection.
method Pseudo-Bayesian framework with scaled Student prior and exponential weights.
result Strong performance compared to Lasso in various settings.
AGM uses adversarial approach for robust prediction in structured prediction problems.
problem Structured prediction problems with complex relationships between variables.
method Adversarial Graphical Models (AGM) for distributionally robust prediction.
result AGM achieves Fisher consistency and flexibility in loss metrics.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
problem Improving pandemic case surge predictions using multimodal data.
method Investigated the effectiveness of biological, public health, and behavioral features.
result Diverse feature sets enhance prediction accuracy, varying by country and phase.
Machine learning predicts circulatory failure in ICU patients.
problem Limited ability of clinicians to recognize early signs of patient deterioration.
method Developed an early warning system using machine learning on ICU data.
result Predicts 90.0% of circulatory failure events with 81.8% identified more than two hours in advance.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of p…
Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This ma…
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural wa…
This paper predicts stock prices using LLMs and news embeddings.
problem Predicting stock prices with high accuracy and relevance.
method Integrates LLMs with stock name embeddings and attention mechanisms for news filtering.
result Reduces MAE by 7.11% compared to baseline.
QTIP improves traffic prediction in sudden disruptions.
problem Traffic models fail during sudden disruptions.
method Simulation-based framework for real-time adaptation.
result QTIP improves traffic prediction in critical minutes of incidents.
Estimates proportions of LLM-generated text in mixed documents.
problem Estimating the proportion of text generated by a pre-specified LLM in mixed documents.
method Developed estimators for two observation regimes: full observation and pivotal reduction, and established sample complexity bounds.
result Full observation estimators require fewer samples than pivotal reduction estimators.
Bayesian model for multi-environment prediction with latent variable changes.
problem Prediction in environments with changing latent variable distributions.
method Bayesian model with empirical Bayes prior and amortized variational algorithm.
result Method outperforms previous approaches in new environments.